{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-learn 中的 RBF 核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mrh5/h+pnaloM3T7o7uu0adatq+fc3zih56NsK7VrC3z6jXDN8sbvXVuqLtHEmZrA4L5O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\nWQsHBjMza/H/AUTAjc/C5ZbKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102f84908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "X, y = datasets.make_moons(noise=0.15, random_state=666)\n",
    "\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "def RBFKernelSVC(gamma):\n",
    "    return Pipeline([\n",
    "        (\"std_scaler\", StandardScaler()),\n",
    "        (\"svc\", SVC(kernel=\"rbf\", gamma=gamma))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=1, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = RBFKernelSVC(gamma=1)\n",
    "svc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z46RXjaoq5TrMbIWZPWZmP03+/mDK60o5n4POjy26N/n+82Z2VVFtG7Kd15jZ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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10928ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=100, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_gamma100 = RBFKernelSVC(gamma=100)\n",
    "svc_gamma100.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0uYfahg1nvBdys1ynjB4FMBV9PAXg74afICIXi8h4/DGATwJ43vG4RJVgD7VZ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103cc70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc_gamma100, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=10, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_gamma10 = RBFKernelSVC(gamma=10)\n",
    "svc_gamma10.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KQy8tjf35pLEGF0bGRlOPNYUwEYvmcxEQfgZghYh8GI1AcBOAv2g9QEQuAPC6qqqIrEOj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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f645a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc_gamma10, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_gamma05 = RBFKernelSVC(gamma=0.5)\n",
    "svc_gamma05.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1K3eie3rCqXbloZJrCAAgLR+G1u/jcm3DWpWDwdKRHbj16eX48hMLcOvTy7F0ZEeuxyvb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\niAIMCEREBIABgYiIAv8Px8ZS71udVJIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103ac9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc_gamma05, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.1, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_gamma01 = RBFKernelSVC(gamma=0.1)\n",
    "svc_gamma01.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xi155fN+q+ydcuHSMm57+DEClJ99Y21WEVgYEAOu7sUj/91lpwlo86nTTnSrWC+od2vmz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\nAAoIIiKSUEAQERFAAUFERBL/HxCWYgISUN0KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10928efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc_gamma01, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  }
 ],
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